Dear Klaus and Steven,
> I have done this and it works. (SPM 96 and SPM "97" Windows).
> Unfortunately my dataset gives identical results, when I use 2
> conditions or 2 replications (1 condition). Which leads me to the
> question: in which order are the various effects entered into the
> model. Or in other words, are there effects that are estimated only
> after other effects have been modelled? For example, confounds
> first, then covariates of interest, then condition and subject effects -
> or all simultaneously? By playing with the data, I found for example
> that SPM estimates of condition effects vary with the covariate of
> interest entered into the model, i.e. they seem to be computed after
> the covariate of interest effect has been taken into account (or
> simultaneously). - Condition effects are also identical whether the
> covariate is entered as a confounder or a covariate of interest.
I think as you have both noted, the inclusion of conditions will affect
the parameter estimates for the covariate (and vice versa) if, and only
if, they are collinear to a certain extent, Least squares fitting of
this sort gives you parameter estimates that you would have obtained
for each effect if you had orthogonalized it with repsect to the
remainder. In this sense there is no order or explicit
orthogonalization but the condition-specific effect will be different
if estimated in the context of a covariate that itself has a
condition-spefici effect (and vice versa). In this instance both
models are right (with and without condition effects) but it is
important to note that if you model them the covariate is only
explaining what cannot be explained by the mean difference between
conditions.
I hope this helps - Karl
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